1. Field of the Invention
The present invention relates to an image processing apparatus and an image processing method.
2. Description of the Related Art
As a conventional method of identifying/detecting an image by allocating representative vectors prepared in advance to feature vectors obtained from the identification/detection target image, L. Fei-Fei and P. Perona, “A Bayesian Hierarchical Model for Learning Natural Scene Categories”, IEEE Comp. Vis. Patt. Recog. 2005 (to be referred to as non-patent reference 1 hereinafter) is known. The method of non-patent reference 1 detects or identifies a target using a feature vector group extracted from an input image. Representative vectors are generated by clustering feature vectors that are extracted from learning data in advance. Identification is done by allocating feature vectors extracted from an identification target image to the indices of neighboring representative vectors.
In non-patent reference 1, the feature vector group extracted from the learning image is preprocessed and divided into a plurality of clusters. Representative vectors are calculated from a feature vector group included in each cluster, and a table storing the representative vectors is generated. In this method, however, feature vector classification greatly affects the identification performance. There have been studied various feature vector classification methods for higher performance, including manifold clustering described in D. Yankov and E Keogh, “Manifold clustering of shapes”, Proc. of ICDM, 2006 (to be referred to as non-patent reference 2 hereinafter) and clustering using a mixed Gaussian distribution described in G. Dorko and C. Schmid, “Object Class Recognition Using Discriminative Local Features”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2004 (to be referred to as non-patent reference 3 hereinafter).
In addition, various examinations have been made to reduce the amount of representative vector calculation or the calculation amount in allocating indices representing representative vectors. In Japanese Patent Laid-Open No. 64-66699, when generating representative vectors, a lookup table for storing representative vector indices corresponding to all coordinates of an input vector space is created. The representative vector indices are allocated by looking up the lookup table, thereby reducing the calculation amount.
However, the methods of non-patent references 2 and 3 require a large calculation amount and also a long processing time for identification. That is, when allocating feature vectors to representative vectors or indices representing representative vectors, it is necessary to execute, even for the feature vectors, processing such as projection to a manifold or mixed Gaussian distribution, which is processing for representative vector generation, and calculate the distances between the feature vectors and the representative vectors. When the calculation amount needed for feature vector classification increases, the calculation amount needed when allocating the feature vectors extracted from an identification target image to representative vectors or indices representing representative vectors also increase, leading to difficulty in real-time identification.
The method of Japanese Patent Laid-Open No. 64-66699 assumes that the input vector space is finite, and distance calculation in representative vector allocation is performed using simple Euclidean distances. This method is therefore hard to implement for a complex method using manifold clustering or the like.